claudeers.
// MCP Servers

jev-use

Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000…

Actively maintained
100/100
last commit 13 days ago
last release 16 days ago
releases 1
open issues 1

Install with your AI

Paste into Claude Code, Cursor, or any agent — it reads the repo and wires the tool into your project.

Install and set up jev-use (claude-plugin project) into my current project.
Found on https://claudeers.com/jev-use
Repo: https://github.com/shitianfang/jev-use
Homepage/docs: https://www.npmjs.com/package/jev-use
Detected install method: claude-plugin → /plugin install jev-use@shitianfang/jev-use
Category: mcp-servers. Platforms: cli, api.
Read the repo's README for exact setup and env vars, then install it and wire it into my project.

Claudeers Health Verdict:
active; community-verified: false. Confirm the source before running anything.
// or install directly (claude-plugin)
/plugin marketplace add shitianfang/jev-use
/plugin install jev-use@shitianfang/jev-use
// or clone
git clone https://github.com/shitianfang/jev-use

// compatibility

Platformscli, api
Operating systems—
AI compatibilityclaude
LicenseMIT
Pricingopen-source
LanguageJavaScript

Get your FREE $2.50 API credits to access TickAtlas financial data ↗

jev-use

English | 简体中文

The best way for Claude Code, Codex, and pi to work with Jev: hand the tasks that need no text output to Jev — faster steps, fewer tokens, tasks done sooner and better.

It makes the LLM and Jev true collaborators: when content needs to be written, the LLM takes over; when a step just needs a fast decision, Jev executes it.

Demos — real runs, 1× speed

Directions task: Jev clicks, the LLM types — 10 decisions (p50 274 ms) · 4 writes; Jev rejects a wrong route, the LLM rewrites
OpenStreetMap directions: Jev picks controls in green, the LLM types the locations in blue; a wrong 1809km geocode is rejected by Jev and repaired by the LLM, ending on the real 3.7km walking route
Context compaction — 200 messages judged in 7 calls, one LLM paragraph replaces the dropped pile; recall 3/3
A real transcript fills the context window to 94%; Jev tints each message keep or drop, the LLM's summary paragraph replaces the dropped block, the window falls to 44% and three recall checks pass
Pong: ball speed = decision latency — 86 Jev decisions in 20 s vs 6 (haiku) and 3 (gemini) called the usual way; enum-constrain both and the gap is 3×
Three Pong lanes replaying a live run at 1x: the Jev ball sweeps the field at ~224ms per decision while the LLM balls crawl
Gate every shell command — dangerous ones denied in ~230 ms with a reason, zero LLM tokens
A 24-command dev session gated at 1x: dangerous commands denied at confidence 1.00, benign ones allowed

Every demo is a rerunnable script in bench/examples/; all numbers, methodology, variance and caveats: bench/RESULTS.md · third-party measurements: docs/evidence.md.

Install

npx -y jev-use install    # wires Claude Code, Codex, and pi — whichever it finds

Set one key in the environment your agent runs in (JEV_BACKEND=mock for a keyless dry run):

ProviderEnv var
TypeSafe directTYPESAFE_API_KEY
OpenRouterOPENROUTER_API_KEY
Vercel AI GatewayAI_GATEWAY_API_KEY

npx -y jev-use doctor checks the wiring. Judged state goes to the provider you configure; JEV_BACKEND=mock stays local. Plugin form with the routing skill and the PreToolUse gate: harness/claude-code · harness/codex.

Use as a library

npm i jev-use — zero runtime dependencies on the judgment path:

import { Jev, check, pick, rate } from "jev-use";

const jev = new Jev();

const { answers } = await jev.judge(state, {
  next: pick("Next action?", { merge: "all green", rerun: "looks flaky", hold: "needs attention" }),
  risk: rate("How risky?", ["routine", "worth a look", "incident"]),
  passed: check("Did the run fully succeed?"),
});
// answers.next → { answer: "merge", confidence: 0.93, confidenceFrom: "reported", escalate: false }

Anything Jev can't or shouldn't decide comes back with escalate: true and a typed reason. Tools, verdict shape, escalation contract, CLI: docs/reference.md.

Small enough to read

FileJob
src/protocol.tsQuestions (check/pick/rate), verdicts, escalation reasons
src/dispatch.tsPre-call routing: what never reaches Jev
src/judge.tsscreen → backend → hand back what is unsure; gate
src/jev.tsThe Jev client over that engine
src/backends/TypeSafe, OpenRouter, Vercel, mock adapters
src/server.tsThe two MCP tools
src/cli.tsinstall, serve, hook gate, doctor
skills/jev-use/SKILL.mdThe routing rules the agent follows

Development

$ npm run typecheck && npm test    # unit tests incl. per-provider wire fixtures
$ npm run smoke                    # real MCP client ↔ built CLI over stdio
$ node bench/run.mjs               # micro-benchmarks, your key and region

Substantially written with Claude Code (AI-assisted).

MIT © shitianfang

// faq

What is jev-use?

Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM. It is open-source on GitHub.

Is jev-use free to use?

jev-use is open-source under the MIT license, so it is free to use.

What category does jev-use belong to?

jev-use is listed under mcp-servers in the Claudeers registry of Claude-compatible tools.

9 views
★ 43 stars
unclaimed
updated 15 days ago

// embed badge

jev-use on Claudeers
[![Claudeers](https://claudeers.com/api/badge/jev-use.svg)](https://claudeers.com/jev-use)

// retro hit counter

jev-use hit counter
[![Hits](https://claudeers.com/api/counter/jev-use.svg)](https://claudeers.com/jev-use)

// reviews

// guestbook

0/500

// related in MCP Servers

🔓

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete…

// mcp-serversf/⟨HTML⟩★ 171,127◷ NOASSERTION[ claude ]
🔓

A cross-platform desktop All-in-One assistant for Claude Code, Codex, OpenCode, OpenClaw, Gemini CLI & Hermes Agent. Only official website: ccswitch.io

// mcp-serversfarion1231/⟨Rust⟩★ 136,484◷ MIT[ claude ]
🔓

🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman

// mcp-serversJuliusBrussee/⟨JavaScript⟩★ 107,719◷ MIT[ claude ]
🔓

An open-source AI agent that brings the power of Gemini directly into your terminal.

// mcp-serversgoogle-gemini/⟨TypeScript⟩★ 107,167◷ Apache-2.0[ claude ]
→ see how jev-use connects across the ecosystem